Day 8: 30 days of Natural Language Processing Series with Projects
NLTK …Part 1
Welcome back peeps. I hope all’s well at your end. For me, last few weeks have been crazy busy and exhausting at work ( and lots of travel). Anyways, let’s hop on to the Day 8 of NLP series with projects. In this post we will be starting with NLTK.
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What is NLTK?
Natural Language Toolkit (NLTK ) is an amazing library for working in linguistics, natural language using Python which lets you analyze linguistic structure, classification, tokenization, stemming, tagging, parsing, and semantic reasoning, corpora, categorizing text etc.
NLTK lets you do following tasks —
- Relationship Extraction
- Sentiment Analysis
- Speech Recognition and Translation
- Topic Segmentation
- Automatic Summarization
- Named Entity Recognition
- Relationship Extraction: This is the process of identifying and extracting relationships between entities in a text, such as the relationship between two people or a person and an organization. This can be useful for tasks such as information extraction, question answering, and knowledge base construction.
- Sentiment Analysis: This is the process of determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral. It can be used to analyze customer reviews, social media posts, and other forms of text to understand public opinion or customer sentiment.
- Speech Recognition and Translation: This is the process of converting speech to text and then translating that text into another language. This can be used for tasks such as speech-to-text dictation, voice commands, and multilingual communication.
- Topic Segmentation: This is the process of dividing a text into different segments, each of which covers a specific topic or theme. This can be useful for tasks such as document summarization, text classification, and information retrieval.
- Automatic Summarization: This is the process of generating a shorter version of a text that retains the most important information. This can be used for tasks such as news summarization, document summarization, and text summarization.
- Named Entities Recognition (NER): This is the process of identifying and classifying named entities, such as people, organizations, and locations, in a text. NER can be used in a variety of applications, such as information extraction, question answering, and text classification.
Let’s get started with NLTK —
To import NLTK
import nltkTokenization
Tokenization is the process to break the text into smaller components/units called as tokens.
from nltk.tokenize import word_tokenize
t = "NewYork is the best city in the world"
tn = word_tokenize(t)To split by space —
text.split(" ")To tokenize a sentence —
nltk.sent_tokenize(doc)Lemmatization & Stemming
Lemmatization and Stemming is used to get the root forms of the text.
Stemming —
from nltk.stem import PorterStemmer
tn = ["NewYork", "is", "a", "beautiful", "city"]
s = PorterStemmer()
sm = [stemmer.stem(tk) for tk in tn] # To create list of stemsLemmatization —
from nltk.stem import WordNetLemmatizer
tn = ["NewYork", "is", "a", "beautiful", "city"]
l = WordNetLemmatizer()
lm = [l.lemmatize(tk) for tk in tn]Stopwords
These are non-content words meant for only grammatical purpose in the text.
from nltk.corpus import stopwords
stp = set(stopwords.words('english'))Parts of Speech
POS is used to assign part of speech to every unit/string in the text.
nltk.pos_tag(words)To work with files
f = open(‘myfile.txt’)
t =file.read();
tn = nltk.word_tokenize(t)
text = nltk.Text(tn)Day 9 and Part 2 of this series : coming soon!
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“Your time is limited, so don’t waste it living someone else’s life.”
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